For bio-inspiration of our model, we focus our attention on: (i) how to form random connections between the AL glomeruli (input layer) and the KCs (hidden layer), and (ii) how to ensure hidden-layer sparsity to best differentiate one odor stimulus from another. To define an arbitrary set of inputs for each KC in the model, the connections between glomeruli and KCs can be represented by sparse, binary, and random matrices [1]}. The firing rates from \(\sim \) 7 randomly selected glomeruli are passed and summed to each KC [1]}, [3]}. For example, [4]} proposed a computational model of the fly olfactory system, including a sparse binary random projection matrix, to generate a tag for each odor composed of a set of neurons that are excited when that odor is presented.
We also refer to that definition and take 7 random indices of the input vector to generate the input for each KC.
